Author Contributions: Drs Meeks and Pereira-Lima had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Meeks and Pereira-Lima contributed equally to the paper and share first author status.

Background Use of intensive longitudinal methods (e.g. ecological momentary assessment, passive sensing) and machine learning (ML) models to predict risk for depression and suicide has increased in recent years. However, these studies often vary considerably in length, ML methods used, and sources of data. The present study examined predictive accuracy for depression and suicidal ideation (SI) as a function of time, comparing different combinations of ML methods and data sources. Methods Participants were 2459 first-year training physicians (55.1% female; 52.5% White) who were provided with Fitbit wearable devices and assessed daily for mood. Linear [elastic net regression (ENR)] and non-linear (random forest) ML algorithms were used to predict depression and SI at the first-quarter follow-up assessment, using two sets of variables (daily mood features only, daily mood features + passive-sensing features). To assess accuracy over time, models were estimated iteratively for each of the first 92 days of internship, using data available up to that point in time. Results ENRs using only the daily mood features generally had the best accuracy for predicting mental health outcomes, and predictive accuracy within 1 standard error of the full 92 day models was attained by weeks 7–8. Depression at 92 days could be predicted accurately (area under the curve >0.70) after only 14 days of data collection. Conclusions Simpler ML methods may outperform more complex methods until passive-sensing features become better specified. For intensive longitudinal studies, there may be limited predictive value in collecting data for more than 2 months.

Background and aimsThe aim of this research was to investigate the relationship between alexithymia and loss-chasing behavior in people at risk and not at risk for problem gambling.MethodsAn opportunity sample of 58 (50 males and 8 females) participants completed the Problem Gambling Severity Index and the Toronto Alexithymia Scale (TAS-20). They then completed the Cambridge Gambling Task from which a measure of loss-chasing behavior was derived.ResultsAlexithymia and problem gambling risk were significantly positively correlated. Subgroups of non-alexithymic and at or near caseness for alexithymia by low risk and at risk for problem gambling were identified. The results show a clear difference for loss-chasing behavior for the two alexithymia conditions, but there was no evidence that low and at-risk problem gamblers were more likely to loss chase. The emotion-processing components of the TAS-20 were shown to correlate with loss chasing.Discussion and conclusionThese findings suggest that loss-chasing behavior may be particularly prevalent in a subgroup of problem gamblers those who are high in alexithymia.

ImportanceEnsuring access to accommodations is critical for resident physicians and their patients. Studies show that a large proportion of medical trainees with disabilities do not request needed accommodations; however, drivers of nonrequests are unknown.ObjectiveTo assess the frequency of accommodation requests among first-year resident physicians (ie, interns) with disabilities and to identify possible drivers of nonrequest for needed accommodations.Design, Setting, and ParticipantsAs part of the Intern Health Study, a longitudinal cohort study of first-year resident physicians, residents at 86 surgical and nonsurgical residency programs in 64 US institutions provided demographic and training characteristics 2 months prior to matriculation (April-May 2021). At the end of their intern year (June 2022), participants completed a new survey with questions about disability-related information, including disability status, disability type, whether they received accommodations, and if not, reasons for nonaccommodation. Poststratification and attrition weights were used to estimate the frequency of accommodation requests and reasons for not requesting accommodations. Interns reporting at least 1 disability were included in the analysis.Main Outcomes and MeasuresPrevalence of reported disabilities, residency specialties distribution, frequency of accommodation requests, and reasons for nonaccommodation among resident physicians with disabilities.ResultsAmong the 1486 resident physicians who completed the baseline survey, 799 (53.8%) replied to the disability questions. Of those, 94 interns (11.8%; weighted number, 173 [11.9%]) reported at least 1 disability and were included in the present study (weighted numbers, 91 [52.6%] men, 82 [47.4%] women, mean [SD] age, 28.6 [3.0] years). Among interns with reported disability and need for accommodations (83 of 173 [48.0%]), more than half (42 [50.6%]) did not request them. The most frequently reported reasons for not requesting needed accommodations were fear of stigma or bias (25 [59.5%]), lack of a clear institutional process for requesting accommodations (10 [23.8%]), and lack of documentation (5 [11.9%]).Conclusions and RelevanceProgram directors should investigate cultural and structural factors within their programs that contribute to an environment where residents do not feel safe or supported in disclosing disability and requesting accommodation and review their disability policies for clarity.

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This cohort study uses survey data to assess the prevalence and development of depressive symptoms among sexual minority and heterosexual physicians during residency training.